Design and Optimization of Intelligent Testing Platform Architecture Based on BERT Model
Currently,software testing,as a crucial step in ensuring the quality of software products,is facing unprecedented challenges.With the increasing complexity of software functions and the shortening of release cycles,traditional manual testing methods are no longer able to meet efficient and accurate testing requirements.Building an intelligent testing platform has become an effective way to solve this problem.This article proposes an intelligent testing platform architecture design based on the BERT model,which can utilize the powerful semantic understanding ability of BERT to automatically generate test cases,prioritize them,predict defects,and conduct in-depth analysis of test results.Firstly,the background and necessity of the intelligent testing platform were introduced,and the basic principles and characteristics of the BERT model were elaborated in detail.Secondly,an intelligent testing platform architecture was proposed,which includes key components such as data preprocessing,BERT model integration,test case generation,test execution and monitoring,and test result analysis and optimization.Thirdly,optimization strategies such as fine-tuning the BERT model for specific testing tasks,multimodal data fusion,and continuous learning and updating were discussed.Finally,the potential of an intelligent testing platform based on the BERT model in improving software testing efficiency and accuracy was summarized.
万方数据
维普
